tff.analytics.heavy_hitters.iblt.decode_iblt_tensor_tf

Decode a IBLT sketch.

tff.analytics.heavy_hitters.iblt.decode_iblt_tensor_tf(
    iblt: tf.Tensor,
    iblt_values: tf.Tensor,
    capacity: int,
    string_max_length: int,
    value_shape: Sequence[int],
    *,
    seed: int = 0,
    repetitions: int = iblt_lib.DEFAULT_REPETITIONS,
    hash_family: Optional[str] = None,
    hash_family_params: Optional[Dict[str, Union[int, float]]] = None,
    field_size: int = iblt_lib.DEFAULT_FIELD_SIZE
) -> Tuple[tf.Tensor, tf.Tensor, tf.Tensor, tf.Tensor]

This function wraps IbltDecoder to decode iblt and returns frequencies of decoded strings.

Args
`iblt`	Tensor representing the IBLT computed by the IbltEncoder.
`iblt_values`	Tensor representing the IBLT values computed by the IbltEncoder.
`capacity`	Number of distinct strings that we expect to be inserted.
`string_max_length`	Maximum length of a string that can be inserted.
`value_shape`	Shape of the values tensor.
`seed`	Integer seed for hash functions. Defaults to 0.
`repetitions`	Number of repetitions in IBLT data structure (must be >= 3). Defaults to 3.
`hash_family`	A `str` specifying the hash family to use to construct IBLT. Options include coupled or random, default is chosen based on capacity.
`hash_family_params`	An optional `dict` of parameters that the hash family hasher expects. Defaults are chosen based on capacity.
`field_size`	The field size for all values in IBLT. Defaults to 2**31 - 1.

Returns
`(out_strings, out_counts, out_tensor_counts num_not_decoded)` where `out_strings` is a `tf.Tensor` containing all the decoded strings, `out_counts` is a `tf.Tensor` containing the counts of each string, `out_tensor_counts` is a `tf.Tensor` containing tensor counts for each decoded string and `num_not_decoded` is a `tf.Tensor` with the number of items not decoded in the IBLT.

tff.analytics.heavy_hitters.iblt.decode_iblt_tensor_tf

Args

Returns